Deep Learning in Heart Disease Diagnosis

Abstract

This review paper delves into the role of Deep Learning (DL) models in the diagnosis of heart diseases, a leading cause of mortality worldwide. Through a comprehensive exploration, we evaluate the application of various DL techniques, including Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), in analyzing diverse data types such as medical images, electronic health records, and genomic sequences. The review illuminates the capabilities of DL in deciphering complex patterns, leading to potential improvements in diagnostic accuracy and patient outcomes. However, we also scrutinize the challenges accompanying DL adoption in clinical settings, such as the need for large, high-quality datasets, the 'black box' problem related to model interpretability, and ethical considerations surrounding data privacy and security. The review concludes with a forward-looking discussion on future research directions, emphasizing the need for multidisciplinary collaboration and ethical vigilance to fully exploit DL's potential in heart disease diagnosis. This paper serves as an essential guide for researchers, clinicians, and policy-makers interested in the intersection of AI and cardiovascular healthcare.